Automation index
How much of a Metrology technician’s job can AI already do?
A CMM program comes earlier. A true dimension without dressing the report do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Metrology technician
22%
AI can already do 22% of this job.
#391 / 633
View rankingMetrology technician
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Program, CMM and the part.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Measure, the stylus and the one that will not reach.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Report, GD&T and not dressing it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Room, the temperature and the soak.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The shop, the rush and not leaving the part dirty.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
A true dimension, and a clean report.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- CMM
- GD&T
- stylus
- measurement programs
- metrology room
- dimensions
- dimensional reports
- soak
Sources for this job· O*NET 17-3028.00· ESCO/ISCO 3111
IS WORK OVER?
ML engineer
29%
#64 / 633
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511